Numerous applications require the movement of a set of robots in a swarm or in packs, whether in an aquatic environment (for example a submarine environment), in an airborne environment or in a land environment. Known applications that can be cited in a submarine environment include the detection of mines or of pollution sources. In these applications, it may be necessary to pilot robots according to different mission profiles (or trajectories) and according to different pack configurations. For example, in the mine detection applications, the robots are preferably piloted in such a way that they are arranged in a line and spaced two-by-two by a fixed distance along this line. A mission profile consists in making them move in a straight line at right angles to the line along which they are spaced. The distance separating two adjacent robots is chosen in such a way as to be at most two times equal to the range of the mine detection means embedded on board the robots, error margins included. This makes it possible to leave no gap in the exploration of a given terrain by the pack concerned.
The piloting of a set of robots in order to make it fulfill its mission is a real issue. In effect, the computation capacities of the robots are, by definition, limited and encourage the use of an approach in which the number of interactions compensate for the lack of computation power. Similarly, the miniaturization of robots induces an increase in the uniformity of capacity thereof that has to be compensated to guarantee the flexibility of the pack. The difficulty in such piloting increases notably when the aim is to maintain a particular pack configuration.
There are a number of piloting methods for ensuring that cohesion is maintained in a swarm. In these conditions, the robots are individualized. Each robot has a role and a predetermined place in the pack. The most obvious solution relies on the centralization of the absolute positions of the robots within a coordinator system (or master system which can be a master robot). The master robot pilots the other robots so as to place them in the positions which are respectively assigned to them in the pack. Another solution consists in making all the robots communicate with one another. In this way, each exchanges its position with all of the robots of the pack and moves to a position which is assigned to it in the pack and relative to the trajectory.
One drawback with these solutions is that they are very costly in communication terms. While communication is relatively easy in air and with a limited number of robots, it is more difficult in water or when the number of robots increases in the pack.
Moreover, with each robot having an assigned place in the pack, a constant piloting has to be applied to all the robots in order to verify that there have been no robots lost, to manage the risks of collision and to schedule the collective movements. This renders the system not very adaptive and not very robust.
Since the positioning of the robots is absolute in a centralized solution, great accuracy is needed for the relative positioning of the robots in the pack to be of acceptable relative accuracy. A rapid exchange of the positions is, additionally, necessary, any latency demands the availability of position anticipation models that are difficult to implement, and sources of errors. Poor relative positioning penalizes the coverage of the terrain and risks creating gaps during its exploration. For example, in a mine detection application in which the range of the mine detection means with which the robots are provided is 5 m, if the distance between the adjacent robots in the pack described above is 10 m, all the terrain in which the robots are moving around is explored. Now, The submarine navigation systems make it possible to determine the absolute position of a robot with an accuracy of only a few meters. It is very difficult to keep the robots spaced apart by 10 m by means of these navigation systems. One solution for avoiding the gaps consists in bringing the robots closer to one another so that their detection areas overlap. However, this leads to exploration systems that are less efficient (it takes longer to scan a given surface terrain) or more costly (to obtain the same efficiencies in terms of exploration time, the number of robots in the pack has to be increased).
Finally, the centralization or the dependency on elaborate communication causes the robustness of the system to be reduced. In effect, in these approaches, the tolerance to failures or the flexibility to adapt to unforeseen events require efforts that are too great to be performed at reasonable cost.